SUMMARY Acute respiratory distress syndrome (ARDS) is a hypoxemic syndrome characterized by disseminated inflammation in the lungs, and often requires prolonged mechanical ventilation and intensive care. Although the hospital mortality of ARDS has decreased, more than 40% of patients do not survive hospitalization. Accelerating progress in treatment requires the ability to reliably attenuate ARDS severity. However, promising therapies for ARDS are often ineffective when tested in clinical trials, due to the fact that current definitions of ARDS select patients who have heterogeneous outcomes, thereby confounding treatment effects. The proposed research exploits recent developments in morphological and functional imaging to investigate integrated strategies for predicting ARDS outcomes and preventing disseminated pulmonary inflammation. A deeper knowledge of the mechanisms that generate severe ARDS will allow us to develop better strategies for clinical management. However, because most research addresses ARDS when it is already established, we do not know how inflammation initially disseminates in the lungs. To better understand the propagation of lung injury, this project will make extensive use of imaging?including quantitative computed tomography (CT), dual-energy CT, and hyperpolarized magnetic resonance imaging (HP MRI)?to spatiotemporally track this process in ARDS models. In many patients, ARDS develops or worsens during mechanical ventilation, in part because the excessive heterogeneity of lung inflation caused by ventilation worsens lung injury. For this reason, we study imaging methodologies which predict progression of experimental lung injury during mechanical ventilation by measuring regional inflation and perfusion. Our primary hypothesis is that healthy areas of the lung can be protected from inflammatory propagation by improving the regional distribution of lung inflation and perfusion, as well as by decreasing tissue edema in injured regions. Using imaging, physiologic, and biological methodologies, we will test this hypothesis in a large animal model of early primary lung injury from acid aspiration. Specifically, this research will: 1) develop a methodology to predict ARDS outcomes and treatment responses from a single set of matched inspiratory-expiratory CT scans; 2) attest to the impact of therapies aimed at spatially containing inflammation before it becomes too severe; and 3) demonstrate that the heterogeneous distribution of lung inflation and perfusion governs the propagation of inflammation in pulmonary tissue. This project will lay the foundation for future applications of imaging-based techniques to predict the risk of severe ARDS and death, attenuate its development, and monitor the progression of lung injury and the effects of novel treatments. The potential to contain primary lung injury in select patients at high risk of adverse outcomes represents a major leap forward in developing effective, personalized approaches to ARDS care.